# get_elts_gauss: The R implementation to get the elements necessary for... In genscore: Generalized Score Matching Estimators

## Description

The R implementation to get the elements necessary for calculations for the gaussian setting on R^p.

## Usage

 1 2 3 4 5 6 7 get_elts_gauss( x, centered = TRUE, profiled_if_noncenter = TRUE, scale = "", diagonal_multiplier = 1 ) 

## Arguments

 x An n by p matrix, the data matrix, where n is the sample size and p the dimension. centered A boolean, whether in the centered setting (assume μ=η=0) or not. Default to TRUE. profiled_if_noncenter A boolean, whether in the profiled setting (λ_η=0) if non-centered. Parameter ignored if centered==TRUE. Default to TRUE. scale A string indicating the scaling method. Returned without being checked or used in the function body. Default to "norm". diagonal_multiplier A number >= 1, the diagonal multiplier.

## Details

For details on the returned values, please refer to get_elts_ab or get_elts.

## Value

A list that contains the elements necessary for estimation.

 n The sample size. p The dimension. centered The centered setting or not. Same as input. scale The scaling method. Same as input. diagonal_multiplier The diagonal multiplier. Same as input. diagonals_with_multiplier A vector that contains the diagonal entries of Γ after applying the multiplier. setting The setting "gaussian". Gamma_K The Γ matrix with no diagonal multiplier. In the non-profiled non-centered setting, this is the Γ sub-matrix corresponding to K. Except for the profiled setting, this is xx'/n. Gamma_K_eta Returned in the non-profiled non-centered setting. The Γ sub-matrix corresponding to interaction between K and η. The minus column means of x. t1,t2 Returned in the profiled non-centered setting, where theη estimate can be retrieved from t1-t2*\hat{K} after appropriate resizing.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 n <- 50 p <- 30 mu <- rep(0, p) K <- diag(p) x <- mvtnorm::rmvnorm(n, mean=mu, sigma=solve(K)) # Equivalently: x2 <- gen(n, setting="gaussian", abs=FALSE, eta=c(K%*%mu), K=K, domain=make_domain("R",p), finite_infinity=100, xinit=NULL, burn_in=1000, thinning=100, verbose=FALSE) elts <- get_elts_gauss(x, centered=TRUE, scale="norm", diag=1.5) elts <- get_elts_gauss(x, centered=FALSE, profiled=FALSE, scale="sd", diag=1.9) 

genscore documentation built on April 28, 2020, 1:06 a.m.